Michael T. Cox

Michael T. Cox

I am a Research Professor of Computer Science and Director of the Collaboration and Cognition Laboratory on the campus of Wright State University. Currently, I serve as a Senior Research Fellow with the ONR Summer Faculty Research Program at The U.S. Naval Research Laboratory, Washington, DC. Previously, I was the founding Director of Autonomy Research at Parallax Advanced Research in Beavercreek, OH, and I was a research scientist at the University of Maryland Institute for Advanced Computer Studies (UMIACS) at College Park, MD. I have also served as a Program Manager in the Information Innovation Office at DARPA, as a Senior Computer Scientist at Raytheon BBN Technologies, Cambridge, MA, and as an Assistant Professor in the Department of Computer Science and Engineering at Wright State in Dayton, OH.

I have broad research interests in artificial intelligence and cognitive systems with respect to high-level reasoning in humans and machines. Technical areas include goal-driven autonomy, volitional explanation, multistrategy learning, case-based reasoning, story understanding, mixed-initiative planning, and computational introspection/metareasoning. In particular, I am interested in how deliberative goals affect behavior across all of these processes. For all cognitive mechanisms, I seek to understand how goals arise and how they change over time.

Research Statement

AAcademic Genealogy

Selected Publications

Cox, M. T. (2005). Metacognition in computation: A selected research review. Artificial Intelligence. 169 (2), 104-141.
Cox, M. T. (2007). Perpetual self-aware cognitive agents. AI Magazine 28(1), 32-45.
Cox, M. T. (2011). Metareasoning, monitoring, and self-explanation. In M. T. Cox & A. Raja (Eds.) Metareasoning: Thinking about thinking (pp. 131-149). Cambridge, MA: MIT Press.
Cox, M. T. (2017). A model of planning, action, and interpretation with goal reasoning. Advances in Cognitive Systems, 5, 57-76.
Cox, M. T. (2020). The Problem with Problems. In Proceedings of the Eighth Annual Conference on Advances in Cognitive Systems. Palo Alto, CA: Cognitive Systems Foundation.
Cox, M. T., Alavi, Z., Dannenhauer, D., Eyorokon, V., Munoz-Avila, H., & Perlis, D. (2016). MIDCA: A metacognitive, integrated dual cycle architecture for self regulated autonomy. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Vol. 5 (pp. 3712-3718). Palo Alto, CA: AAAI Press.
Cox, M. T., & Dannenhauer, D. (2016). Goal transformation and goal reasoning. In Proceedings of the 4th Workshop on Goal Reasoning. New York, IJCAI-16.
Cox, M. T., Dannenhauer, D., & Kontrakunta, S. (2017). Goal operations for cognitive systems. In Proceedings of the Thirty-first AAAI Conference on Artificial Intelligence (pp. 4385-4391). Palo Alto, CA: AAAI Press.
Cox, M. T., & Dannenhauer, Z. A. (2017). Perceptual goal monitors for cognitive agents in changing environments. In Proceedings of the Fifth Annual Conference on Advances in Cognitive Systems, Poster Collection. (pp. 1-16). Palo Alto, CA: Cognitive Systems Foundation.
Cox, M. T., Mohammad, Z., Kondrakunta, S., Gogineni, V. R., Dannenhauer, D. & Larue, O. (2021). Computational metacognition. In Proceedings of the Ninth Annual Conference on Advances in Cognitive Systems. Palo Alto, CA: Cognitive Systems Foundation. arXiv:2201.12885
Cox, M. T., Oates, T., Paisner, M., & Perlis, D. (2012). Noting anomalies in streams of symbolic predicates using A-distance. Advances in Cognitive Systems 2, 167-184.
Dannenhauer, D., Cox, M. T., & Munoz-Avila, H. (2018). Declarative metacognitive expectations for high-level cognition. Advances in Cognitive Systems, 6, 231-250.
Dannenhauer, D., Munoz-Avila, H., & Cox, M. T. (2016). Informed expectations to guide GDA agents in partially observable environments. In S. Kambhampati (Ed.), Proceedings of the 25th International Joint Conference on Artificial Intelligence (pp. 2493-2499). Palo Alto, CA: AAAI Press / International Joint Conferences on Artificial Intelligence, Inc.
Dannenhauer, D., Munoz-Avila, H., & Cox, M. T. (2020). Expectations for agents with goal-driven autonomy. Journal of Experimental and Theoretical Artificial Intelligence.
Dannenhauer, Z. A., & Cox, M. T. (2018). Rationale-based perceptual monitors. AI Communications, 31 (2), 197-212.
Eyorokon, V., Yalamanchili, P., & Cox, M. T. (2018). Tangent recognition and anomaly pruning to trap off-topic questions in conversational case-based dialogues. In M. Cox, P. Funk, & S. Begum (Eds.), Case-Based Reasoning Research and Development: Proceedings of the 26th International Conference (pp.213-228). Berlin: Springer.
Kondrakunta, S., Gogineni, V. R., Molineaux, M., Munoz-Avila, H., Oxenham, M., & Cox, M. T. (2018). Toward problem recognition, explanation and goal formulation. In Working Notes of the 2018 IJCAI Goal Reasoning Workshop. IJCAI.
Paisner, M., Cox, M. T., Maynord, M., & Perlis, D. (2014). Goal-driven autonomy for cognitive systems. In Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 2085–2090). Austin, TX: Cognitive Science Society.
Siler, J. C., & Cox, M. T. (2018). Generating plans for qualitative goal preferences. In Working Notes of the 2018 IJCAI Goal Reasoning Workshop. IJCAI.